Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling

Robots designed to interact socially with people require reliable estimates of human position and motion. Additional pose data such as body orientation may enable a robot to interact more effectively by providing a basis for inferring contextual social information such as people's intentions and relationships. To this end, we have developed a system for simultaneously tracking the position and body orientation of many people, using a network of laser range finders mounted at torso height. An individual particle filter is used to track the position and velocity of each human, and a parametric shape model representing the person's cross-sectional contour is fit to the observed data at each step. We demonstrate the system's tracking accuracy quantitatively in laboratory trials and we present results from a field experiment observing subjects walking through the lobby of a building. The results show that our method can closely track torso and arm movements, even with noisy and incomplete sensor data, and we present examples of social information observable from this orientation and positioning information that may be useful for social robots.     

一种应用于在线标定的可观测度分析方法

对惯性器件误差标定前必须进行可观测性分析,针对当前可观测性分析方法不能得到每一个误差参数的可观测度,无法为标定过程中载体的机动路径设计提供足够依据这一问题,提出了一种新的动态系统可观测度分析方法并用于惯性器件在线标定中。该方法定义各误差参数都独立对应一个可观测度,通过合理拆分可观测矩阵,利用范数最优化受约束理论,计算得到每一个参数的可观测度。利用MATLAB软件搭建仿真平台,对比分析了基于奇异值分解的可观测度分析方法和新方法计算结果的特点,并利用新方法计算结果针对性调整载体机动路径,试验结果表明:利用该方法得到的各误差参数的可观测度与标定中各状态量的滤波收敛特性具有一致性,证明了该算法的特点及准确性。     

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.     

A Robust Solution to the Stereo-Vision-Based Simultaneous Localization and Mapping Problem with Steady and Moving Landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot–landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.     

Photographic Processing with One-Time Use of Solutions

Most industrial processing machines, in the motion-picture laboratories, as well as in the large photofinishing stations, tend to be very large and to contain hundreds of gallons of solutions. This tendency seems to be right when considering the high chemical inertia resulting from such big volumes, but its corollary is the progressive and unrelenting chemical evolution of the solutions. So as to avoid this an artificial equilibrium is maintained by continuous replenishing with a suitably compounded solution, and an overflow of used liquid. This method is not very economical, and requires a rigorous chemical control and leads to tank formulae which are not necessarily the most suitable for the emulsion to be processed.

One-time use of solutions, on the contrary, allows the chemical equilibrium to be established by the initial formula itself, which can, therefore, be exactly adapted to the emulsion considered. This system, which makes chemical control unnecessary, is very economical provided that the overflowing solution volume is entirely exhausted, which is true only for a high ratio of processed surface to solution volume.     

Attitude Stabilization of a Biologically Inspired Robotic Housefly via Dynamic Multimodal Attitude Estimation

In this paper, we study sensor fusion for the attitude stabilization of micro aerial vehicles, particularly mechanical flying insects. Following a geometric approach, a dynamic observer is proposed that estimates attitude based on kinematic data available from different and redundant bioinspired sensors such as halteres, ocelli, gravitometers, magnetic compass and light polarization compass. In particular, the traditional structure of complementary filters, suitable for multiple sensor fusion, is specialized to the Lie group of rigid-body rotations SO(3). The filter performance based on a three-axis accelerometer and a three-axis gyroscope is experimentally tested on a 2-d.o.f. support, showing its effectiveness. Finally, attitude stabilization is proposed based on a feedback scheme with dynamic estimation of the state, i.e., the orientation and the angular velocity. Almost-global stability of the proposed controller in the case of dynamic state estimation is demonstrated via the separation principle, and realistic numerical simulations with noisy sensors and external disturbances are provided to validate the proposed control scheme.     

Multi-Sensor-Based Control Strategy for Initiating and Maintaining Interaction Between a Robot and a Human

This paper presents a multi-sensor-based control strategy allowing a mobile robot to safely navigate with respect to a given human being. Two sensors are embedded in our robot: a vision system that is able to detect and track the person of interest, and a RFID antennas belt that can locate the tag worn by the latter. Thus, our control strategy will be built using image features (when the user is visible) and RFID information (when not). In the first case, a robust visual servoing control will be designed, while in the second case a suitable RFID controller will be proposed. Experimental results demonstrate the efficiency of the proposed control strategy.     

A Novel High-Speed and High-Accuracy Manipulator of Planar Five-Link Structure: Modeling and Calibration

This paper describes a novel manipulator of planar five-link structure, which is designed to be characterized as high speed and high accuracy. The manipulator's mathematic models are built for purposes of analysis and control. Due to the lack of high-accuracy angular measurement equipment during manufacturing, the exact initial joint positions of the manipulator are unknown. An extended Kalman filter-based calibration algorithm is proposed to estimate those initial angles and a laser interferometer is adopted as the measurement equipment. To satisfy the high-speed requirement, a new Cartesian trajectory which combines the trapezoidal and S-shape trajectories is proposed. The testing results show that the requirements with respect to velocity, acceleration and accuracy are successfully achieved. Finally, an application example is given to demonstrate its potential uses.